Clinical Utility of Magnetoencephalography in Epilepsy Evaluation: A Qualitative Systematic Review
Bibliographic record
Abstract
Magnetoencephalography (MEG) is a non-invasive neurophysiological technique offering high spatial resolution for localizing epileptogenic zones in epilepsy, especially when traditional electroencephalography or magnetic resonance imaging (MRI) is inconclusive. A systematic evaluation of MEG's diagnostic and prognostic utility within combination strategies is crucial, particularly in countries like South Korea with limited MEG access. We conducted a qualitative systematic review of nine studies (n=354 focal epilepsy patients) to evaluate MEG's clinical performance in presurgical workup. Databases (MEDLINE, EMBASE, Cochrane, KoreaMed, KMbase, RISS) were searched. Data extraction focused on localization accuracy and surgical outcomes (Engel class I); risk of bias was assessed using quality assessment of diagnostic accuracy studies-2. MEG alone achieved up to the mid-70% range; however, integration with other modalities (e.g., with positron emission tomography/high-density electroencephalography) significantly improved both localization and surgical outcomes. Pediatric focal cortical dysplasia patients showed Engel class I outcomes of 67-87%. Most studies had low-to-moderate bias. Only one MEG system is operational in South Korea (introduced 2023), limiting accessibility. Canadian economic evaluations, despite higher initial costs, suggest MEG is long-term cost-effective, improving quality-adjusted life years. MEG offers complementary diagnostic value in epilepsy evaluation and surgical planning, enhancing localization and outcome prediction, especially for pediatric and MRI-negative patients. Considering this clinical utility, national support for MEG equipment and its regional expansion in South Korea is crucial to ensure equitable access and optimal patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".